Master class: Estimating and correcting for measurement error in longitudinal studies

3rd of June 2021

14:00 – 18:15 (BST)

Online

To mark the launch of the new book Measurement Error in Longitudinal Data the Royal Statistical Society will be hosting a half-day Master class. The Master class will bring together six of the book authors to present the state of the art in statistical models for estimating and correcting for measurement error in longitudinal data. The course will highlight different statistical models as well as how these can be implemented in practice.

Book your place now on the RSS website.

 Program – 3rd of June 2021: 14:00 – 18:15 (BST) – online

14:00 – 14:15Introduction to event and book
Alexandru Cernat (University of Manchester) & Joe Sakshaug (IAB/LMU-Munich)
  
14:15 – 14:50Developing Reliable Measures: An Approach to Evaluating the Quality of Survey Measurement Using Longitudinal Designs
Duane F. Alwin (Pennsylvania State University)
  
14:50 – 15:25Comparison of Reliability in Seventeen European Countries Using the Quasi-Simplex Model
Johana Chylíková (Institute of Sociology of the Czech Academy of Sciences)
  
15:25 – 16:00Modelling error dependence in categorical longitudinal data
Dimitris Pavlopoulos (Vrije Universiteit Amsterdam)
  
16:00 – 16:15Break
  
16:15 – 16:50Longitudinal Measurement (Non)Invariance in Latent Constructs
Heinz Leitgöb (University of Eichstätt-Ingolstadt)
  
16:50 – 17:25Self-evaluation, Differential Item Functioning and Longitudinal Anchoring Vignettes
Omar Paccagnella (University of Padua)
  
17:25 – 18:00Establishing measurement invariance across time within an accelerated longitudinal design
Maria Pampaka (University of Manchester)
  
18:00 – 18:15Q&A and concluding remarks

As part of the event, Oxford University Press will be offering a 30% on the Measurement Error in Longitudinal Data book.


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.